Prediction and evaluation of enteric methane emissions from lactating dairy cows using different levels of covariate information

2016 ◽  
Vol 56 (3) ◽  
pp. 557 ◽  
Author(s):  
B. Santiago-Juarez ◽  
L. E. Moraes ◽  
J. A. D. R. N. Appuhamy ◽  
W. F. Pellikaan ◽  
D. P. Casper ◽  
...  
Keyword(s):  
Prediction Models ◽  
Total Error ◽  
Predictive Ability ◽  
Information Criteria ◽  
Mitigation Strategies ◽  
Mixed Effect ◽  
Acceptable Error ◽  
Ch4 Emissions ◽  
Enteric Methane ◽  
Level 2

The dairy sector contributes to global warming through enteric methane (CH4) emissions. Methane is also a loss of energy to the ruminant. Several studies have developed CH4 prediction models to assess mitigation strategies to reduce emissions. However, the majority of these models have low predictive ability or require numerous inputs that are often not readily available in commercial dairy operations. In this context, the objective of the present paper was to develop CH4 prediction models by using varying levels of information available at the farm level. The seven complexity levels used the following information: (1) dietary nutrient composition, (2) milk yield and composition, (3) Levels 1 and 2, (4) Level 3 plus dry matter intake (DMI), (5) Level 4 plus bodyweight, (6) Level 2 plus DMI, and (7) DMI only. Models were fitted to 489 individual enteric-CH4 measurements from 30 indirect calorimetry studies and evaluated with an independent database comprising 215 treatment means from 62 studies collected from the literature. Within each complexity level, all possible mixed-effect models were fitted and those with the lowest values of Akaike or Bayesian information criteria were selected using lme4 package in R. Models were evaluated using mean square prediction error (MSPE) based statistic, root MSPE (RMSPE) to observation standard deviation ratio, concordance correlation coefficient and Nash–Sutcliffe efficiency methods. All fitted models performed well with an acceptable error estimates (RMSPE as a percentage of observed mean (RMSPE%) = 16–24%), with more than two-thirds of total error originating from random bias. Overall, models with DMI were more accurate (RMSPE% = 16–20%) than those without (RMSPE% = 20–24%). Although the best prediction model (RMSPE% = 16%) was developed using Level 5 information, a model using Level 2 information is recommended for on-farm methane estimates if DMI is not measured. The proposed models offer easy and practical tools to dairy producers for predicting CH4 emissions and evaluating CH4 mitigation strategies.

Using highly nutritious pastures to mitigate enteric methane emissions from cattle grazing systems in South America

2018 ◽  
Vol 58 (12) ◽  
pp. 2329 ◽  
Author(s):  
Y. Dini ◽  
J. I. Gere ◽  
C. Cajarville ◽  
Verónica S. Ciganda
Keyword(s):  
Crude Protein ◽  
Dry Matter ◽  
Mitigation Strategies ◽  
Dry Matter Intake ◽  
Ether Extract ◽  
High Quality ◽  
Neutral Detergent Fibre ◽  
Ch4 Emissions ◽  
Dry Matter Digestibility ◽  
Enteric Methane

Enteric methane (CH4) emissions are directly related to the quantity and type of feed intake. Existing mitigation strategies, for example, the addition of legumes to grass-based diets and increased use of grains, have been thoroughly researched and applied in different production systems. In this paper, we propose a need to expand the capacity to mitigate enteric CH4 emissions in cattle under grazing conditions. The objective of this paper was to contribute to evaluate a mitigation strategy under grazing conditions of using contrasting levels of pasture quality. The study was performed with 20 heifers twice during the year: winter and spring. Each season, the study employed a crossover design with two treatments and two 5-day measurement periods. The treatments were two pastures with different nutritional values, including a pasture with a low quality (70% of neutral detergent fibre, 1% of ether extract, 8% of non-fibre carbohydrates), 9% of crude protein, 35% of dry matter digestibility and a pasture with a high quality (42% neutral detergent fibre, 1.3% ether extract, 24% non-fibre carbohydrates, 21% crude protein and 63% dry matter digestibility). Enteric CH4 emissions were measured with sulfur hexafluoride tracer technique. The dry matter intake (kg/day) was measured indirectly using titanium dioxide as an external marker. CH4 emissions from animals grazing the high-quality pasture were 14% lower expressed as % of gross energy intake, and 11% lower expressed by unit of dry matter intake (g CH4/kg). These results quantitative showed the alternative to mitigate CH4 emissions from grazing bovines exclusively through the improvement of the forage quality offered.

scholarly journals A Review of Enteric Methane Emission Measurement Techniques in Ruminants

Animals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 1004
Author(s):  
Yiguang Zhao ◽  
Xuemei Nan ◽  
Liang Yang ◽  
Shanshan Zheng ◽  
Linshu Jiang ◽  
...  
Keyword(s):  
Large Scale ◽  
Measurement Techniques ◽  
Mitigation Strategies ◽  
Emission Measurement ◽  
Accumulation Chamber ◽  
Ch4 Emissions ◽  
Advantages And Disadvantages ◽  
Ch4 Production ◽  
Animal Training ◽  
Enteric Methane

To identify relationships between animal, dietary and management factors and the resulting methane (CH4) emissions, and to identify potential mitigation strategies for CH4 production, it is vital to develop reliable and accurate CH4 measurement techniques. This review outlines various methods for measuring enteric CH4 emissions from ruminants such as respiration chambers (RC), sulphur hexafluoride (SF6) tracer, GreenFeed, sniffer method, ventilated hood, facemask, laser CH4 detector and portable accumulation chamber. The advantages and disadvantages of these techniques are discussed. In general, RC, SF6 and ventilated hood are capable of 24 h continuous measurements for each individual animal, providing accurate reference methods used for research and inventory purposes. However, they require high labor input, animal training and are time consuming. In contrast, short-term measurement techniques (i.e., GreenFeed, sniffer method, facemask, laser CH4 detector and portable accumulation chamber) contain additional variations in timing and frequency of measurements obtained relative to the 24 h feeding cycle. However, they are suitable for large-scale measurements under commercial conditions due to their simplicity and high throughput. Successful use of these techniques relies on optimal matching between the objectives of the studies and the mechanism of each method with consideration of animal behavior and welfare. This review can provide useful information in selecting suitable techniques for CH4 emission measurement in ruminants.

scholarly journals 172 Enteric methane emissions from pregnant beef heifers as affected by multiple mitigation strategies

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 131-131
Author(s):  
Rhea E Teranishi ◽  
E J McGeough ◽  
Karin Wittenberg ◽  
Gary Crow ◽  
Kim Ominski
Keyword(s):  
Low Cost ◽  
Average Daily Gain ◽  
Latin Square ◽  
Dietary Treatment ◽  
Mitigation Strategies ◽  
Beef Heifers ◽  
Ch4 Emissions ◽  
Enteric Methane ◽  
Grass Hay ◽  
Dietary Treatments

Abstract This study was conducted to determine if enteric methane (CH4) emissions from pregnant beef heifers could be reduced by using multiple dietary mitigation strategies. The trial was designed as a 4 x 4 Latin square consisting of a 21-d adaptation phase followed by a 21-d data collection phase. Forty Aberdeen Angus cross pregnant beef heifers were randomly assigned to one of the four dietary treatments (n = 10): i) low protein grass hay (L; 6.1% CP; 1.9% fat); ii) adequate protein grass hay (AD; 10.8% CP; 1.9% fat); iii) AD supplemented with sunflower screenings (ADSS; 9.8% CP; 5.6% fat); and iv) above adequate protein legume grass mix hay (AAD; 12.8% CP; 1.7% fat). Total dry matter intake (DMI) was 32%, 27% and 39% greater (P < 0.0001) for AD, ADSS and AAD respectively, relative to the L diet (6.5 kg d-1). Average daily gain (ADG; kg d-1) was influenced by dietary treatment (P < 0.0001), as heifers offered L, AD, ADSS and AAD diets gained 0 ± 0.2, 0.6 ± 0.2, 0.5 ± 0.2 and 0.7 ± 0.2 kg d-1, respectively. Enteric CH4 emissions (L d-1), were influenced by dietary treatment (P < 0.0001) with 184 ± 18.9, 214 ± 19.0, 204 ± 19.1 and 232 ± 19.1 L d-1 for heifers offered L, AD, ADSS and AAD diets, respectively. Further, heifers offered AD, ADSS and AAD diets emitted 19%, 22% and 14% less (P=0.03) enteric CH4 (% GEI) relative to diet L, respectively. This study demonstrates that supplementation to meet nutrient requirements for protein or increasing the fat content of forage-based diets using low-cost by-products such as sunflower screenings can decrease enteric CH4 emissions without adversely impacting total DMI and ADG.

scholarly journals Risk prediction in multicentre studies when there is confounding by cluster or informative cluster size

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Menelaos Pavlou ◽  
Gareth Ambler ◽  
Rumana Z. Omar
Keyword(s):  
Risk Prediction ◽  
Cluster Size ◽  
Linear Models ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Clustered Data ◽  
Predictor Variable ◽  
Simulated Data ◽  
Predictive Ability ◽  
Informative Cluster Size

Abstract Background Clustered data arise in research when patients are clustered within larger units. Generalised Estimating Equations (GEE) and Generalised Linear Models (GLMM) can be used to provide marginal and cluster-specific inference and predictions, respectively. Methods Confounding by Cluster (CBC) and Informative cluster size (ICS) are two complications that may arise when modelling clustered data. CBC can arise when the distribution of a predictor variable (termed ‘exposure’), varies between clusters causing confounding of the exposure-outcome relationship. ICS means that the cluster size conditional on covariates is not independent of the outcome. In both situations, standard GEE and GLMM may provide biased or misleading inference, and modifications have been proposed. However, both CBC and ICS are routinely overlooked in the context of risk prediction, and their impact on the predictive ability of the models has been little explored. We study the effect of CBC and ICS on the predictive ability of risk models for binary outcomes when GEE and GLMM are used. We examine whether two simple approaches to handle CBC and ICS, which involve adjusting for the cluster mean of the exposure and the cluster size, respectively, can improve the accuracy of predictions. Results Both CBC and ICS can be viewed as violations of the assumptions in the standard GLMM; the random effects are correlated with exposure for CBC and cluster size for ICS. Based on these principles, we simulated data subject to CBC/ICS. The simulation studies suggested that the predictive ability of models derived from using standard GLMM and GEE ignoring CBC/ICS was affected. Marginal predictions were found to be mis-calibrated. Adjusting for the cluster-mean of the exposure or the cluster size improved calibration, discrimination and the overall predictive accuracy of marginal predictions, by explaining part of the between cluster variability. The presence of CBC/ICS did not affect the accuracy of conditional predictions. We illustrate these concepts using real data from a multicentre study with potential CBC. Conclusion Ignoring CBC and ICS when developing prediction models for clustered data can affect the accuracy of marginal predictions. Adjusting for the cluster mean of the exposure or the cluster size can improve the predictive accuracy of marginal predictions.

A review of literature on risk prediction tools for hospital readmissions in older adults

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Michelle Louise Gatt ◽  
Maria Cassar ◽  
Sandra C. Buttigieg
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Hospital Readmissions ◽  
Predictive Ability ◽  
Chronic Obstructive ◽  
Risk Category ◽  
Extensive Literature ◽  
Content Type ◽  
Prediction Tools ◽  
Readmission Risk

Purpose The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.Design/methodology/approach Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.Findings Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5–0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.Research limitations/implications Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.Originality/value This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

scholarly journals Relative fat mass, a new index of adiposity, is strongly associated with incident heart failure: data from PREVEND

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Navin Suthahar ◽  
Laura M. G. Meems ◽  
Coenraad Withaar ◽  
Thomas M. Gorter ◽  
Lyanne M. Kieneker ◽  
...  
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Fat Mass ◽  
Prediction Models ◽  
Age Groups ◽  
Information Criteria ◽  
Anthropometric Indices ◽  
Reduced Ejection Fraction ◽  
Adiposity Indices ◽  
Incident Heart Failure

AbstractBody-mass index (BMI), waist circumference, and waist-hip ratio are commonly used anthropometric indices of adiposity. However, over the past 10 years, several new anthropometric indices were developed, that more accurately correlated with body fat distribution and total fat mass. They include relative fat mass (RFM), body-roundness index (BRI), weight-adjusted-waist index and body-shape index (BSI). In the current study, we included 8295 adults from the PREVEND (Prevention of Renal and Vascular End-Stage Disease) observational cohort (the Netherlands), and sought to examine associations of novel as well as established adiposity indices with incident heart failure (HF). The mean age of study population was 50 ± 13 years, and approximately 50% (n = 4134) were women. Over a 11 year period, 363 HF events occurred, resulting in an overall incidence rate of 3.88 per 1000 person-years. We found that all indices of adiposity (except BSI) were significantly associated with incident HF in the total population (P < 0.001); these associations were not modified by sex (P interaction > 0.1). Amongst adiposity indices, the strongest association was observed with RFM [hazard ratio (HR) 1.67 per 1 SD increase; 95% confidence interval (CI) 1.37–2.04]. This trend persisted across multiple age groups and BMI categories, and across HF subtypes [HR: 1.76, 95% CI 1.26–2.45 for HF with preserved ejection fraction; HR 1.61, 95% CI 1.25–2.06 for HF with reduced ejection fraction]. We also found that all adiposity indices (except BSI) improved the fit of a clinical HF model; improvements were, however, most evident after adding RFM and BRI (reduction in Akaike information criteria: 24.4 and 26.5 respectively). In conclusion, we report that amongst multiple anthropometric indicators of adiposity, RFM displayed the strongest association with HF risk in Dutch community dwellers. Future studies should examine the value of including RFM in HF risk prediction models.

scholarly journals Adding to Fire Fighter Safety by Including Real-Time Radar Data in Short-Range Forecasts of Thunderstorm-Induced Wind Shifts

Fire ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 55
Author(s):  
Gary L. Achtemeier ◽  
Scott L. Goodrick
Keyword(s):  
Real Time ◽  
Short Range ◽  
Prediction Models ◽  
Weather Prediction ◽  
Radar Data ◽  
Mitigation Strategies ◽  
Density Currents ◽  
Gust Fronts ◽  
Gust Front ◽  
Range Forecasts

Abrupt changes in wind direction and speed caused by thunderstorm-generated gust fronts can, within a few seconds, transform slow-spreading low-intensity flanking fires into high-intensity head fires. Flame heights and spread rates can more than double. Fire mitigation strategies are challenged and the safety of fire crews is put at risk. We propose a class of numerical weather prediction models that incorporate real-time radar data and which can provide fire response units with images of accurate very short-range forecasts of gust front locations and intensities. Real-time weather radar data are coupled with a wind model that simulates density currents over complex terrain. Then two convective systems from formation and merger to gust front arrival at the location of a wildfire at Yarnell, Arizona, in 2013 are simulated. We present images of maps showing the progress of the gust fronts toward the fire. Such images can be transmitted to fire crews to assist decision-making. We conclude, therefore, that very short-range gust front prediction models that incorporate real-time radar data show promise as a means of predicting the critical weather information on gust front propagation for fire operations, and that such tools warrant further study.

scholarly journals Empirics of Korean Shipping Companies’ Default Predictions

Risks ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 159
Author(s):  
Sunghwa Park ◽  
Hyunsok Kim ◽  
Janghan Kwon ◽  
Taeil Kim
Keyword(s):  
Logit Model ◽  
Prediction Models ◽  
Predictive Ability ◽  
Financial Ratios ◽  
Probability Of Default ◽  
Shipping Industry ◽  
Market Participants ◽  
Korean Economy ◽  
Default Prediction ◽  
Financial Characteristics

In this paper, we use a logit model to predict the probability of default for Korean shipping companies. We explore numerous financial ratios to find predictors of a shipping firm’s failure and construct four default prediction models. The results suggest that a model with industry specific indicators outperforms other models in predictive ability. This finding indicates that utilizing information about unique financial characteristics of the shipping industry may enhance the performance of default prediction models. Given the importance of the shipping industry in the Korean economy, this study can benefit both policymakers and market participants.

scholarly journals An IMERG-Based Optimal Extended Probabilistic Climatology (EPC) as a Benchmark Ensemble Forecast for Precipitation in the Tropics and Subtropics

2021 ◽  
Author(s):  
Eva–Maria Walz ◽  
Marlon Maranan ◽  
Roderick van der Linden ◽  
Andreas H. Fink ◽  
Peter Knippertz
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Predictive Ability ◽  
Window Size ◽  
Predictive Performance ◽  
Ensemble Forecast ◽  
High Mountain ◽  
Rainfall Occurrence ◽  
Precipitation Measurement ◽  
Global Precipitation

AbstractCurrent numerical weather prediction models show limited skill in predicting low-latitude precipitation. To aid future improvements, be it with better dynamical or statistical models, we propose a well-defined benchmark forecast. We use the arguably best currently high-resolution, gauge-calibrated, gridded precipitation product, the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) (IMERG) “final run” in a ± 15-day window around the date of interest to build an empirical climatological ensemble forecast. This window size is an optimal compromise between statistical robustness and flexibility to represent seasonal changes. We refer to this benchmark as Extended Probabilistic Climatology (EPC) and compute it on a 0.1°×0.1° grid for 40°S–40°N and the period 2001–2019. In order to reduce and standardize information, a mixed Bernoulli-Gamma distribution is fitted to the empirical EPC, which hardly affects predictive performance. The EPC is then compared to 1-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) using standard verification scores. With respect to rainfall amount, ECMWF performs only slightly better than EPS over most of the low latitudes and worse over high-mountain and dry oceanic areas as well as over tropical Africa, where the lack of skill is also evident in independent station data. For rainfall occurrence, EPC is superior over most oceanic, coastal, and mountain regions, although the better potential predictive ability of ECMWF indicates that this is mostly due to calibration problems. To encourage the use of the new benchmark, we provide the data, scripts, and an interactive webtool to the scientific community.

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